The Role of Attributional and Distributional Information in Semantic Representation

In recent studies of semantic representation, two distinct sources of information from which we can learn word meanings have been described. We refer to these as attributional and distributional information sources. Attributional information describes the attributes or features associated with referents of words, and is acquired from our interactions with the world. Distributional information describes the distribution of words across different linguistic contexts, and is acquired through our use of language. While previous work has concentrated on the role of one source, to the exclusion of the other, in this paper we study the role of both sources in combination. We describe a general framework based on probabilistic generative models for modelling both sources of information, and how they can be integrated to learn semantic representation. We provide examples comparing the learned structures of each of three models: attributional information alone, distributional information alone, and both sources combined.